trello-api-1-use-batch-operations
Sub-skill of trello-api: 1. Use Batch Operations (+3).
Best use case
trello-api-1-use-batch-operations is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of trello-api: 1. Use Batch Operations (+3).
Teams using trello-api-1-use-batch-operations should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/1-use-batch-operations/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How trello-api-1-use-batch-operations Compares
| Feature / Agent | trello-api-1-use-batch-operations | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of trello-api: 1. Use Batch Operations (+3).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 1. Use Batch Operations (+3)
## 1. Use Batch Operations
```python
# GOOD: Batch operations when possible
import requests
def batch_create_cards(list_id, cards):
"""Create multiple cards efficiently."""
for card in cards:
requests.post(
"https://api.trello.com/1/cards",
data={
"key": API_KEY,
"token": TOKEN,
"idList": list_id,
**card
}
)
# Small delay to avoid rate limits
time.sleep(0.1)
# AVOID: Individual requests without batching consideration
```
## 2. Handle Rate Limits
```python
import time
from functools import wraps
def rate_limit(max_per_second=10):
"""Rate limit decorator."""
min_interval = 1.0 / max_per_second
last_call = [0.0]
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
elapsed = time.time() - last_call[0]
if elapsed < min_interval:
time.sleep(min_interval - elapsed)
result = func(*args, **kwargs)
last_call[0] = time.time()
return result
return wrapper
return decorator
@rate_limit(max_per_second=10)
def api_call(endpoint, **kwargs):
return requests.get(endpoint, params=kwargs)
```
## 3. Cache Board Data
```python
from functools import lru_cache
from datetime import datetime, timedelta
class TrelloCache:
def __init__(self, ttl_seconds=300):
self.ttl = ttl_seconds
self.cache = {}
def get_board(self, client, board_id):
key = f"board_{board_id}"
now = datetime.now()
if key in self.cache:
data, timestamp = self.cache[key]
if now - timestamp < timedelta(seconds=self.ttl):
return data
board = client.get_board(board_id)
self.cache[key] = (board, now)
return board
```
## 4. Error Handling
```python
import requests
from requests.exceptions import RequestException
def safe_trello_request(method, url, **kwargs):
"""Make Trello API request with error handling."""
try:
response = requests.request(method, url, **kwargs)
response.raise_for_status()
return response.json()
except requests.exceptions.HTTPError as e:
if e.response.status_code == 429:
# Rate limited - wait and retry
time.sleep(10)
return safe_trello_request(method, url, **kwargs)
elif e.response.status_code == 401:
raise Exception("Invalid API credentials")
else:
raise
except RequestException as e:
raise Exception(f"Network error: {e}")
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